{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# LSTM RNN"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn import model_selection\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout\n",
        "\n",
        "from tensorflow.python.util import deprecation\n",
        "deprecation._PRINT_DEPRECATION_WARNINGS = False\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.filterwarnings(\"ignore\", message=r\"Passing\", category=FutureWarning)\n",
        "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
        "warnings.filterwarnings('ignore', category=FutureWarning)\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        }
      ],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:15.222Z",
          "iopub.execute_input": "2022-07-31T02:21:15.226Z",
          "iopub.status.idle": "2022-07-31T02:21:17.167Z",
          "shell.execute_reply": "2022-07-31T02:21:17.160Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2020-01-01'\n",
        "end = '2022-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume\nDate                                                                       \n2020-01-02  46.860001  49.250000  46.630001  49.099998  49.099998  80331100\n2020-01-03  48.029999  49.389999  47.540001  48.599998  48.599998  73127400\n2020-01-06  48.020000  48.860001  47.860001  48.389999  48.389999  47934900\n2020-01-07  49.349998  49.389999  48.040001  48.250000  48.250000  58061400\n2020-01-08  47.849998  48.299999  47.139999  47.830002  47.830002  53767000",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-02</th>\n      <td>46.860001</td>\n      <td>49.250000</td>\n      <td>46.630001</td>\n      <td>49.099998</td>\n      <td>49.099998</td>\n      <td>80331100</td>\n    </tr>\n    <tr>\n      <th>2020-01-03</th>\n      <td>48.029999</td>\n      <td>49.389999</td>\n      <td>47.540001</td>\n      <td>48.599998</td>\n      <td>48.599998</td>\n      <td>73127400</td>\n    </tr>\n    <tr>\n      <th>2020-01-06</th>\n      <td>48.020000</td>\n      <td>48.860001</td>\n      <td>47.860001</td>\n      <td>48.389999</td>\n      <td>48.389999</td>\n      <td>47934900</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>49.349998</td>\n      <td>49.389999</td>\n      <td>48.040001</td>\n      <td>48.250000</td>\n      <td>48.250000</td>\n      <td>58061400</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>47.849998</td>\n      <td>48.299999</td>\n      <td>47.139999</td>\n      <td>47.830002</td>\n      <td>47.830002</td>\n      <td>53767000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:17.172Z",
          "iopub.execute_input": "2022-07-31T02:21:17.176Z",
          "iopub.status.idle": "2022-07-31T02:21:18.504Z",
          "shell.execute_reply": "2022-07-31T02:21:18.584Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:18.510Z",
          "iopub.execute_input": "2022-07-31T02:21:18.514Z",
          "iopub.status.idle": "2022-07-31T02:21:18.520Z",
          "shell.execute_reply": "2022-07-31T02:21:18.587Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "                 Open       High        Low      Close  Adj Close    Volume  \\\nDate                                                                          \n2020-01-03  48.029999  49.389999  47.540001  48.599998  48.599998  73127400   \n2020-01-06  48.020000  48.860001  47.860001  48.389999  48.389999  47934900   \n2020-01-07  49.349998  49.389999  48.040001  48.250000  48.250000  58061400   \n2020-01-08  47.849998  48.299999  47.139999  47.830002  47.830002  53767000   \n2020-01-09  48.939999  49.959999  48.389999  48.970001  48.970001  76512800   \n\n            Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                                                 \n2020-01-03                  0                 0         0 -0.010183  \n2020-01-06                  1                 1         0 -0.004321  \n2020-01-07                  0                 0         0 -0.002893  \n2020-01-08                  1                 1         1 -0.008705  \n2020-01-09                  0                 1         0  0.023834  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-03</th>\n      <td>48.029999</td>\n      <td>49.389999</td>\n      <td>47.540001</td>\n      <td>48.599998</td>\n      <td>48.599998</td>\n      <td>73127400</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.010183</td>\n    </tr>\n    <tr>\n      <th>2020-01-06</th>\n      <td>48.020000</td>\n      <td>48.860001</td>\n      <td>47.860001</td>\n      <td>48.389999</td>\n      <td>48.389999</td>\n      <td>47934900</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-0.004321</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>49.349998</td>\n      <td>49.389999</td>\n      <td>48.040001</td>\n      <td>48.250000</td>\n      <td>48.250000</td>\n      <td>58061400</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.002893</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>47.849998</td>\n      <td>48.299999</td>\n      <td>47.139999</td>\n      <td>47.830002</td>\n      <td>47.830002</td>\n      <td>53767000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.008705</td>\n    </tr>\n    <tr>\n      <th>2020-01-09</th>\n      <td>48.939999</td>\n      <td>49.959999</td>\n      <td>48.389999</td>\n      <td>48.970001</td>\n      <td>48.970001</td>\n      <td>76512800</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.023834</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:18.525Z",
          "iopub.execute_input": "2022-07-31T02:21:18.529Z",
          "iopub.status.idle": "2022-07-31T02:21:18.538Z",
          "shell.execute_reply": "2022-07-31T02:21:18.590Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n2021-12-27  147.509995  154.889999  147.250000  154.360001  154.360001   \n2021-12-28  155.880005  156.729996  151.380005  153.149994  153.149994   \n2021-12-29  152.820007  154.339996  147.289993  148.259995  148.259995   \n2021-12-30  147.440002  148.850006  144.850006  145.149994  145.149994   \n2021-12-31  146.160004  148.610001  143.550003  143.899994  143.899994   \n\n              Volume  Increase_Decrease  Buy_Sell_on_Open  Buy_Sell   Returns  \nDate                                                                           \n2021-12-27  53296400                  1                 1         0  0.056247  \n2021-12-28  58699100                  0                 0         0 -0.007839  \n2021-12-29  51300200                  0                 0         0 -0.031929  \n2021-12-30  44358000                  1                 0         0 -0.020977  \n2021-12-31  49448100                  0                 0         0 -0.008612  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Returns</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-12-27</th>\n      <td>147.509995</td>\n      <td>154.889999</td>\n      <td>147.250000</td>\n      <td>154.360001</td>\n      <td>154.360001</td>\n      <td>53296400</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.056247</td>\n    </tr>\n    <tr>\n      <th>2021-12-28</th>\n      <td>155.880005</td>\n      <td>156.729996</td>\n      <td>151.380005</td>\n      <td>153.149994</td>\n      <td>153.149994</td>\n      <td>58699100</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.007839</td>\n    </tr>\n    <tr>\n      <th>2021-12-29</th>\n      <td>152.820007</td>\n      <td>154.339996</td>\n      <td>147.289993</td>\n      <td>148.259995</td>\n      <td>148.259995</td>\n      <td>51300200</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.031929</td>\n    </tr>\n    <tr>\n      <th>2021-12-30</th>\n      <td>147.440002</td>\n      <td>148.850006</td>\n      <td>144.850006</td>\n      <td>145.149994</td>\n      <td>145.149994</td>\n      <td>44358000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.020977</td>\n    </tr>\n    <tr>\n      <th>2021-12-31</th>\n      <td>146.160004</td>\n      <td>148.610001</td>\n      <td>143.550003</td>\n      <td>143.899994</td>\n      <td>143.899994</td>\n      <td>49448100</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.008612</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:18.544Z",
          "iopub.execute_input": "2022-07-31T02:21:18.547Z",
          "iopub.status.idle": "2022-07-31T02:21:18.555Z",
          "shell.execute_reply": "2022-07-31T02:21:18.594Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,14))\n",
        "plt.plot(dataset['Adj Close'])\n",
        "plt.title('Historical Stock Value')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Stock Price')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1008x1008 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:18.563Z",
          "iopub.execute_input": "2022-07-31T02:21:18.566Z",
          "iopub.status.idle": "2022-07-31T02:21:18.700Z",
          "shell.execute_reply": "2022-07-31T02:21:18.755Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "(504, 10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:18.705Z",
          "iopub.execute_input": "2022-07-31T02:21:18.709Z",
          "shell.execute_reply": "2022-07-31T02:21:18.759Z",
          "iopub.status.idle": "2022-07-31T02:21:18.719Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = dataset.iloc[:, 1:2].values\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(train_set)\n",
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(100,504):\n",
        "    X_train.append(training_set_scaled[i-100:i, 0])\n",
        "    y_train.append(training_set_scaled[i, 0]) \n",
        "X_train, y_train = np.array(X_train), np.array(y_train)\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:18.725Z",
          "iopub.execute_input": "2022-07-31T02:21:18.728Z",
          "iopub.status.idle": "2022-07-31T02:21:18.735Z",
          "shell.execute_reply": "2022-07-31T02:21:18.762Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regressor = Sequential()\n",
        "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(LSTM(units = 50))\n",
        "regressor.add(Dropout(0.2))\n",
        "regressor.add(Dense(units = 1))"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:18.741Z",
          "iopub.execute_input": "2022-07-31T02:21:18.744Z",
          "iopub.status.idle": "2022-07-31T02:21:19.346Z",
          "shell.execute_reply": "2022-07-31T02:21:19.366Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
        "regressor.fit(X_train, y_train, epochs = 15, batch_size = 32)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "404/404 [==============================] - 2s 5ms/step - loss: 0.0653\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 2/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0154\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 3/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0101\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 4/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0081\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 5/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0080\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 6/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0073\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 7/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0073\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 8/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0067\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 9/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0058\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 10/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0067\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 11/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0059\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 12/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0072\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 13/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0061\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 14/15\n",
            "404/404 [==============================] - 1s 2ms/step - loss: 0.0063\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 15/15\n",
            "404/404 [==============================] - 1s 3ms/step - loss: 0.0055\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "<keras.callbacks.callbacks.History at 0x263715ebb38>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:19.353Z",
          "iopub.execute_input": "2022-07-31T02:21:19.356Z",
          "iopub.status.idle": "2022-07-31T02:21:36.665Z",
          "shell.execute_reply": "2022-07-31T02:21:36.680Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testdataframe= yf.download(symbol,start=\"2022-01-01\",end=\"2022-07-30\")\n",
        "testdataframe['Date'] = testdataframe.index\n",
        "testdata = pd.DataFrame(columns = ['Date', 'Open', 'High', 'Low', 'Adj Close'])\n",
        "testdata['Date'] = testdataframe['Date']\n",
        "testdata['Open'] = testdataframe['Open']\n",
        "testdata['High'] = testdataframe['High']\n",
        "testdata['Low'] = testdataframe['Low']\n",
        "testdata['Close'] = testdataframe['Adj Close']\n",
        "real_stock_price = testdata.iloc[:, 1:2].values\n",
        "dataset_total = pd.concat((dataset['Open'], testdata['Open']), axis = 0)\n",
        "inputs = dataset_total[len(dataset_total) - len(testdata) - 100:].values\n",
        "inputs = inputs.reshape(-1,1)\n",
        "inputs = sc.transform(inputs)\n",
        "X_test = []\n",
        "for i in range(100, 150):\n",
        "    X_test.append(inputs[i-100:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:36.672Z",
          "iopub.execute_input": "2022-07-31T02:21:36.676Z",
          "iopub.status.idle": "2022-07-31T02:21:36.848Z",
          "shell.execute_reply": "2022-07-31T02:21:36.864Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_stock_price = regressor.predict(X_test)\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:36.854Z",
          "iopub.execute_input": "2022-07-31T02:21:36.859Z",
          "iopub.status.idle": "2022-07-31T02:21:37.060Z",
          "shell.execute_reply": "2022-07-31T02:21:37.073Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.plot(real_stock_price, color = 'green', label = 'Stock Price')\n",
        "plt.plot(predicted_stock_price, color = 'red', label = 'Predicted Stock Price')\n",
        "plt.title(symbol + ' Stock Price Prediction')\n",
        "plt.xlabel('Trading Day')\n",
        "plt.ylabel(symbol + ' Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1440x720 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-07-31T02:21:37.066Z",
          "iopub.execute_input": "2022-07-31T02:21:37.077Z",
          "iopub.status.idle": "2022-07-31T02:21:37.199Z",
          "shell.execute_reply": "2022-07-31T02:21:37.205Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}